Deep Q-Network Based Route Scheduling for Transportation Network Company Vehicles

Abstract

The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.